COVID-19 vaccination single cell datasets

<p>The datasets presented here comprise the sequencing data featured in the research paper titled: &quot;Multimodal single-cell datasets characterize antigen-specific CD8⁺&nbsp;T cells across SARS-CoV-2 vaccination and infection&quot;: https://www.nature.com/articles/s41590-023-01608-9</p> <p>Peripheral Blood Mononuclear Cell (PBMC) samples utilized for both CITE-seq and ASAP-seq were systematically collected at four distinct time intervals:</p> <ul> <li>Pre-vaccination (Day 0)</li> <li>Post-primary vaccination (Day 2 and Day 10.</li> <li>Seven days post-boost vaccination (Day 28).</li> </ul> <p>The count matrix folder contains&nbsp;count matrices for each experimental type, specifically CITE-seq, ASAP-seq, and ECCITE-seq. In addition, we have included the fully integrated, processed Seurat objects for downstream analysis.</p> <p>Details of the content within the count matrix folder are as follows:</p> <ul> <li>The RNA, ATAC, and TCR modality outputs were generated using the 10x Cellranger pipeline.</li> <li>HTO and ADT modalities were mapped with Alevin.</li> </ul> <p>Outlined below are the three processed single-cell datasets:</p> <ol> <li>PBMC_vaccine_CITE.rds: 3&#39; RNA and surface proteins (173 TotalSeq-A antibodies)</li> <li>PBMC_vaccine_ASAP.rds: Chromatin accessibility&nbsp;and surface proteins&nbsp;(173 TotalSeq-A antibodies)</li> <li>PBMC_vaccine_ECCITE_TCR.rds:&nbsp;5&#39; RNA, surface proteins (137 TotalSeq-C antibodies), TCR and dextramer loaded with peptides of SARS-CoV-2 spike protein.</li> </ol> <ul> <li>antigen_module_genes.rds: This file contains the vaccine-induced gene sets.</li> <li>antigen_module_peaks.rds: This file contains the DE peaks specific for vaccine-induced cells.&nbsp;</li> </ul> <p>To map the&nbsp;scRNA-seq query dataset onto our CITE-seq reference:</p> <pre><code>library(Seurat) PBMC_CITE &lt;- readRDS("/zenedo/PBMC_vaccine_CITE.rds") query_scRNA &lt;- readRDS("/home/xx/your_own_data.rds") anchors &lt;- FindTransferAnchors( reference = PBMC_CITE, query = query_scRNA, normalization.method = "SCT", k.anchor = 5, reference.reduction = "spca", dims = 1:50) query_scRNA &lt;- MapQuery( anchorset = anchors, query = query_scRNA, reference = PBMC_CITE, refdata = list( l1 = "celltypel1", l2 = "celltypel2", l3 = "celltypel3"), reference.reduction = "spca", reduction.model = "wnn.umap") </code></pre> <p>To use the scATAC-seq data, please run the commands below to update the path of the fragment file for the object.&nbsp;&nbsp;</p> <pre><code>Vaccine_ASAP &lt;- readRDS("PBMC_vaccine_ASAP.rds") # remove fragment file information Fragments(Vaccine_ASAP) &lt;- NULL # Update the path of the fragment file Fragments(Vaccine_ASAP) &lt;- CreateFragmentObject(path = "download/PBMC_vaccine_ASAP_fragments.tsv.gz", cells = Cells(Vaccine_ASAP))</code></pre> <p>&nbsp;</p>